# 🧠 Mental Health Dashboard

A Streamlit-based dashboard for **exploratory data analysis (EDA)** and **analysis of mental health datasets**.  
The project includes interactive visualizations, correlation analysis, and insights into factors affecting mental health risk.

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📂 Project Structure

mental_health/

mental_health/data/mental_health.csv             # Raw dataset
mental_health/data/mental_health_cleaned.csv     # Cleaned dataset
mental_health/pages/EDA.py                       # Exploratory Data Analysis page
mental_health/pages/Analysis_Dashboard.py        # Core analysis dashboard page
mental_health/utils/data_processing.py           # Data loading & cleaning functions
mental_health/app.py                             # Main Streamlit entry point
mental_health/mental_health.py                   # Data cleaning script
mental_health/requirements.txt                   # Dependencies
mental_health/README.md                          # Project documentation

🚀 Getting Started

1. Clone the repository

git clone https://github.com/hyarisara/mental_health_dashboard.git
cd mental_health_dashboard

2. Create a virtual environment and install dependencies

python -m venv .venv
# Activate the venv:
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate

pip install -r requirements.txt

3. Run the dashboard

streamlit run app.py

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📊 Features

• Interactive EDA for numeric and categorical variables
• Correlation analysis and heatmaps
• Analysis of stress, sleep, and physical activity interaction
• Social support effect on depression/anxiety
• Identification of high-risk groups
• Suggestion of potential predictors for mental health risk

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🛠 How to Contribute

1. Fork the repository
2. Create a new branch

git checkout -b feature/your-feature-name

3. Make your changes
4. Commit your changes

git commit -m "Add new feature"

5. Push to your branch

git push origin feature/your-feature-name

6. Open a Pull Request

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📚 Learning Resources

• Streamlit Docs – Build interactive dashboards
• Seaborn Docs – Visualizations and plots
• Pandas Docs – Data manipulation
• Scipy Stats – Statistical analysis